Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findings

Walsh, Melissa; Wallace, Gregory L.; Gallegos, Stephen M.; Braden, B. Blair

doi:10.1016/j.nicl.2021.102719

Cited by 52 publications

(48 citation statements)

References 180 publications

(391 reference statements)

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“…The number of studies examining gender differences in ASD at the brain level is minimal, 4 and the findings from these studies have been largely inconsistent: some studies have reported gender differences in functional brain organisation in ASD, whereas others have not found any. 3,4 Critically, the findings from these studies remain poorly replicated, likely because of the small numbers of participants, especially female participants. 3,4 To address this, we examine, to the best of our knowledge, one of the largest functional brain imaging data-sets to date of females and males with ASD obtained from multiple sites across the world-Autism Brain Imaging Data Exchange (ABIDE) 6 -along with the data we have collected, 7 as well as an independent Child Mind Institute-Health Brain Network (CMI-HBN) cohort, 8 using a novel explainable artificial intelligence 9 (XAI)-based framework.…”

Section: Study Aimsmentioning

confidence: 99%

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism

et al. 2022

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Background Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences. Aims To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity. Method We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups. Results stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD. Conclusions Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.

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Section: Study Aimsmentioning

confidence: 99%

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism

et al. 2022

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Section: Discussionmentioning

confidence: 99%

“…The observed "male-typical" patterns supporting camouflaging in ASD-F are consistent with the fetal steroidogenic hypothesis, [81][82][83][84][85] given that such exposure in females would result in "male-typical" brain features. 12 However, the growing literature on sex differences in the brain suggests that both masculine and feminine processes interact with individual genetics and environment across development in a time-sensitive manner to produce an individual's brain "mosaic." [88][89][90] In keeping, the "female-typical" FC patterns linked to camouflaging suggest that female sex chromosomes or reproductive biology (e.g., ovarian hormones) also influence social compensatory behavior in ASD-F and, potentially, the broader sex bias in ASD.…”

Section: Discussionmentioning

confidence: 99%

“…11 A recent systematic review highlights that ASD-F show more atypical brain features than ASD-M, but some atypical features may be "protective." 12 For example, the left anterior prefrontal cortex has shown more atypical features in ASD-F. 13 However, left anterior prefrontal functional connectivity (FC) with reward circuitry may buffer against ASD genetic risk in females. 14 A novel way to elucidate brain systems underlying the sex bias in ASD is to characterize sex-related brain correlates of behaviors that show a sex bias in ASD.…”

Section: Introductionmentioning

confidence: 99%

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Sex-Related Neurocircuitry Supporting Camouflaging in Adults with Autism: Female Protection Insights

Walsh

Pagni

Monahan

et al. 2021

Preprint

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The male preponderance in autism led to the hypothesis that aspects of female biology are protective against autism. Females with autism report engaging in more compensatory behaviors (i.e., “camouflaging”) to overcome autism-related social differences, which may be a downstream result of protective pathways. No studies have examined sex-related brain pathways supporting camouflaging in females with autism, despite its potential to inform mechanisms underlying the sex bias in autism.This case-control design included 45 non-intellectually-disabled adults with autism (male/female: 21/24) and 40 neurotypical adults (male/female: 19/21) ages 18-71. We used group multivariate voxel pattern analysis to conduct a data-driven, connectome-wide characterization of “sex-atypical” (sex-by-diagnosis) and “sex-typical” (sex) brain functional connectivity features linked to camouflaging, and validated findings in females with autism multi-modally via structural connectometry. Exploratory associations with cognitive control, memory, emotion recognition, and depression/anxiety examined the adaptive nature of functional connectivity patterns supporting camouflaging in females with autism.We found 1) “sex-atypical” functional connectivity patterns predicting camouflaging in the hypothalamus and precuneus and 2) “sex-typical” patterns in the anterior cingulate and right anterior parahippocampus. Higher hypothalamic functional connectivity with a limbic reward cluster was the strongest predictor of camouflaging in females with autism (a “sex-atypical” pattern), and also predicted better cognitive control/emotion recognition. Structural connectometry validated functional connectivity results with consistent brain pathways/effect patterns implicated across multi-modal findings in females with autism.This data-driven, connectome-wide characterization of “sex-atypical” and “sex-typical” brain connectivity features supporting compensatory social behavior in autism suggests hormones may play a role in the autism sex bias. Furthermore, both “male-typical” and “female-typical” brain connectivity patterns are implicated in camouflaging in females with autism in circuits associated with reward, emotion, and memory processing. “Sex-atypical” results are consistent with the fetal steroidogenic hypothesis, which would result in masculinized brain features in females with autism. However, female genetics/biology may contribute to “female-typical” patterns implicated in camouflaging.

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“…Ref. [51] argue that there is current evidence from neuroimaging studies that sex differences in ASD are age-dependent. The authors concluded that studies should focus on large-sample studies and a lifespan perspective.…”

Section: Applicationmentioning

confidence: 99%

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder

Ribeiro

Vidal

Sato

et al. 2021

Entropy

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Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls.

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Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findings

Cited by 52 publications

References 180 publications

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism

Sex-Related Neurocircuitry Supporting Camouflaging in Adults with Autism: Female Protection Insights

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder

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